Related papers: Location based Probabilistic Load Forecasting of E…
In order to foster electric vehicle (EV) adoption, there is a strong need for designing and developing charging stations that can accommodate different customer classes, distinguished by their charging preferences, needs, and technologies.…
This paper presents an experimental investigation and performance evaluation of a hybrid electric radio-controlled car powered by a Nickel-Metal Hydride battery combined with a renewable Proton Exchange Membrane Fuel Cell system. The study…
The scheduling and operation of power system becomes prominently complex and uncertain, especially with the penetration of distributed power. Load forecasting matters to the effective operation of power system. This paper proposes a novel…
Electric vehicles (EVs) add significant load on the power grid as they become widespread. The characteristics of this extra load follow the patterns of people's driving behaviours. In particular, random parameters such as arrival time and…
Task offloading is of paramount importance to efficiently orchestrate vehicular wireless networks, necessitating the availability of information regarding the current network status and computational resources. However, due to the mobility…
Electric vehicles (EVs) represent the long-term green substitute for traditional fuel-based vehicles. To encourage EV adoption, the trust of the end-users must be assured. In this work, we focus on a recently emerging privacy threat of…
Electric Vehicles (EVs) offer substantial flexibility for grid services, yet large-scale, uncoordinated charging can threaten voltage stability in distribution networks. Existing Reinforcement Learning (RL) approaches for smart charging…
Enabling widespread electric vehicle (EV) adoption requires substantial build-out of charging infrastructure in the coming decade. We formulate the charging infrastructure needs as a scaling analysis problem and use it to estimate the EV…
Electric vehicles (EVs) are increasingly used in transportation. Worldwide use of EVs, for their limited battery capacity, calls for effective planning of EVs charging stations to enhance the efficiency of using EVs. This paper provides a…
The availability of charging stations is an important factor for promoting electric vehicles (EVs) as a carbon-friendly way of transportation. Hence, for city planners, the crucial question is where to place charging stations so that they…
Mid-term electricity load forecasting (LF) plays a critical role in power system planning and operation. To address the issue of error accumulation and transfer during the operation of existing LF models, a novel model called error…
Electric trucks are increasingly deployed to reduce the trucking sector's carbon footprint, but their limited range and charging needs create operational challenges on mid- to long-haul routes. Truck platooning can mitigate range anxiety…
The increasing demand for direct electric energy in the grid is also tied to the increase of Electric Vehicle (EV) usage in the cities, which eventually will totally substitute combustion engine Vehicles. Nevertheless, this high amount of…
Accurate forecasting of recovery rates (RR) is central to credit risk management and regulatory capital determination. In many loan portfolios, however, RR modeling is constrained by data scarcity arising from infrequent default events.…
Pure electric vehicles (PEVs) are increasingly adopted to decarbonize the transport sector and mitigate global warming. However, the inadequate PEV charging infrastructure may hinder the further adoption of PEVs in the large-scale traffic…
With the advances in the Internet of Things technology, electric vehicles (EVs) have become easier to schedule in daily life, which is reshaping the electric load curve. It is important to design efficient charging algorithms to mitigate…
Multivariate time series forecasting enables the prediction of future states by leveraging historical data, thereby facilitating decision-making processes. Each data node in a multivariate time series encompasses a sequence of multiple…
This paper presents a Temporal Convolutional Network (TCN) based hybrid PV forecasting framework for enhancing hours-ahead utility-scale PV forecasting. The hybrid framework consists of two forecasting models: a physics-based trend…
We use simulation to compare different power flow models in the process of charging electric vehicles (EVs) by considering their random arrivals, their stochastic demand for energy at charging stations, and the characteristics of the…
A major barrier to wide adoption of Electric Vehicles (EVs) is the absence of reliable and equitable charging infrastructure. Poorly located charging stations create coverage gaps and slow down EV adoption, especially in underserved…